The invention relates to the general field of machine vision based inspection technology and more particularly to machine vision based detection and classification of defects occurring on large flat patterned surfaces. In particular, the invention addresses the inspection of materials deposited on large substrate glass plates such as Liquid Crystal Display (LCD) panels. Although the invention applies to the general case of the inspection of any flat patterned media, the invention is particularly related to the inspection of glass substrates used for Thin Film Transistor (TFT) LCD panels in precompletion form.
During the manufacturing of LCD panels, large clear sheets of thin glass are used as a substrate for the deposition of various layers of materials to form electronic circuits that will function as a multitude of identical display panels. This deposition is usually done in stages wherein, at each stage, a particular material, such as metal, Indium Tin Oxide (ITO), silicon, or amorphous silicon, is deposited over a previous layer (or upon the glass substrate) in adherence to a predetermined pattern often determined by a mask. Each process stage includes various steps such as deposition, masking, etching and stripping.
During each of the process stages and at various steps within a stage, various production defects may be introduced that affect the structure and that have electronic and/or visual implications on the final LCD panel product. Such defects include but are not limited to circuit shorts, opens, foreign particles, mask problems, feature size problems, over etching and under etching. In order for the final LCD panel to operate properly, these defects need to be detected, classified and if possible repaired, preferably at the stage they are generated. The decision to repair is based on accurate classification of defects and especially to the separation between “killer”, “repairable” and “process” defects.
The operating resolution of the system for automatic defect detection often has a direct impact on the inspection speed and the cost of the system. Therefore, only comparatively lower resolutions are feasible to scan the entire area of the plate. Unfortunately, at this lower resolution it has not been possible in the past to simultaneously perform both detection and reliable classification from the same image data collected. Also, low resolution has an impact on the performance of the detection algorithms, which often result in a substantial number of false alarms which need to be eliminated. Therefore, following a defect detection step, there is a need for a defect review step, wherein a higher resolution inspection (via camera) is used to capture the defect area of interest for subsequent validation of the defect candidate and thereafter to perform either automatic or human assisted classification.
In this type of operation, a low resolution sub-system is used to detect the problem areas of the plate (Defect Detection Sub-System—DDS) while a separate high resolution camera is used at a later stage to capture high resolution images for these problem areas (Defect Review Sub-System—DRS) for the purpose of higher reliability automatic or manual classification. As long as the number of problem areas detected by the DDS can be kept within manageable limits, high resolution image capture for these singular points remains feasible. Still, this number has often a direct impact on either the cycle time of the system (if all defects are to be reviewed) or on the review performance of the system (if a fixed limited number of defects are reviewed).
Automated Optical Inspection (AOI) equipment has been used for a variety of problems including but not limited to Printed Circuit Board (PCB) inspection, silicon Very Large Scale Integrated (VLSI) circuit wafer (die) inspection, as well as LCD panel inspection. Most of the implemented solutions are based on spatial domain pattern comparison techniques often used in combination with sensor level pixel or sub-pixel precision alignment techniques.
U.S. Pat. No. 4,579,455 to Levy et al. describes an alignment and pattern comparison technique where a pair of 7×7 windows are considered on the test and reference images and squared sum of errors over a multitude of possible 3×3 sub-windows within this window are computed. If the minimum error over these combinations exceeds a threshold value, a defect is assumed. The method appears to be capable of compensating for alignment mismatch down to a sensor pixel level.
Addressing issues about the coarse alignment precision of the method by Levy et al., U.S. Pat. No. 4,805,123 to Specht et al. describes an improved alignment and comparison technique for the detection of defects. In this technique, large windows in test and reference images are used to compute a sensor pixel level correlation between test and reference. The resulting sampled correlation surface's minimum point is found and a quadratic function is fitted to the surface in the neighborhood of this minimum point. Using the fitted quadratic function, a sub-pixel precision translation is obtained to align the test and reference images. The aligned images are compared by thresholding image differences on 2×2 sub-windows on the test and aligned reference images.
Variations and improvements on these basic techniques have also been proposed such as U.S. Pat. No. 5,907,628 to Yolles et al., which among other things point out to the drawbacks of using the sampled correlation surface to find the minimum and argue that due to coarse sampling of the surface this point may not correspond to the true minimum. Hence, they argue that the subsequent sub-pixel interpolation step would do little to improve the detected minimum and a false alignment would result, leading to false alarms in detection. Yolles et al. proposes to alleviate these problems by an elaborate comparison process based on improved comparison entities.
With any of the above methods, the use of a single feasible (comparatively low) resolution for scanning the entire surface of the article inspected leads to a set of defect candidates. These defect candidates necessarily include both legitimate defects as well as false alarms, due to the inability of the methods to completely filter out expected variations between a test and reference image. This results in the alarm to be a set of defect candidates and arises the need to validate the candidates to form the true defect map of the article inspected. Furthermore, there is a strong need to classify the legitimate defects into a number of defect classes to help with the disposition of the article inspected, in some applications possibly enabling the article to be repaired.
One solution along these lines has been proposed by U.S. Pat. No. 5,699,447 to Alumot et al. which describes a two stage scanning approach. The entire panel is first scanned at a higher speed by a stage of a comparatively low resolution non-Charge Coupled Device (CCD) optical system with a small diameter laser beam in a raster scan mode. This is followed by a second stage scan with a high resolution CCD-based optical system. The latter scanning stage extracts the higher resolution images of all the defect suspect locations which have been detected by the former scanning stage. Although this solution addresses the need to extract higher resolution images of the detected objects for their validation, it is different from the current invention in that the first stage of examination is by means of a raster scan with a small diameter laser beam and also for the fact that the two examinations are in sequential stages. The disadvantages include the following:
(a) Increased cycle time: The high resolution imaging stage comes immediately after the main detection stage. The sequential nature of the high resolution imaging has considerable impact on the inspection cycle time since the time required for high resolution image acquisition, review and classification is added to the time required for the detection scan.
(b) Idle imaging resources: The high resolution defect review imager is idle while the detection imager is active and the detection imager is idle while the review imager is active. This leads to an inefficient use of the system resources within the given time constraints.
(c) Non-optimal review process: When classification is necessary, and production environment time constraints prevent the imaging of all candidate locations, the user of the system may be left with the difficult task of deciding how many and which of the candidates to collect and process with the high resolution review imager.
What is needed is a fully automatic and overlapped high resolution defect review system operating in combination with a rapid and accurate classification technique to improve both the speed and accuracy of inspection and repair.